A precision-recall curve is a graphical representation used to evaluate the performance of a binary classifier, showing the trade-off between precision (the accuracy of positive predictions) and recall (the ability to find all positive instances) across different thresholds. It is particularly useful in scenarios with imbalanced datasets, where the positive class is rare, as it focuses on the performance of the positive class rather than the overall accuracy.